Add dataset card and metadata for RandSATBench

README.md

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+---
+license: mit
+task_categories:
+- graph-ml
+---
+
+# RandSATBench: Benchmarks for Constraint Satisfaction Problems
+
+This repository contains the benchmarks presented in the paper [Benchmarking Graph Neural Networks in Solving Hard Constraint Satisfaction Problems](https://huggingface.co/papers/2602.18419).
+
+**RandSATBench** provides a mixed set of easy, hard, and unsatisfiable instances of constraint satisfaction problems, specifically *q-coloring* and *Boolean satisfiability* (K-SAT) problems. The goal is to provide a challenging setting to compare the performance of deep learning methods (particularly Graph Neural Networks) against classical exact and heuristic solvers.
+
+- **Paper:** [https://huggingface.co/papers/2602.18419](https://huggingface.co/papers/2602.18419)
+- **Repository:** [https://github.com/ArtLabBocconi/RandCSPBench](https://github.com/ArtLabBocconi/RandCSPBench)
+
+## Datasets
+
+The benchmark includes datasets for Boolean satisfiability (3-SAT and 4-SAT) and coloring problems (3-coloring and 5-coloring). The instances vary in size ($N=16, 32, 64, 256$) and connectivity.
+
+| Dataset | # Train Instances | # Test Instances |
+|---------|-------------------|------------------|
+| 3-SAT   | 168,000           | 42,000           | 
+| 4-SAT   | 84,000            | 21,000           | 
+| 3-col   | 60,000            | 20,000           | 
+| 5-col   | 60,000            | 20,000           | 
+
+## Usage
+
+### K-SAT
+
+The random instances of 3-SAT or 5-SAT problems in CNF format can be downloaded using the following:
+
+```bash
+wget https://huggingface.co/datasets/CarloLucibello/kSAT-Benchmarks/resolve/main/kSAT.zip
+unzip kSAT.zip
+```
+
+Ground truth solutions obtained by running the CaDiCal solver are available in the corresponding `train_labels.csv` files.
+
+### q-coloring
+
+To generate the random graphs for the 3-coloring and 5-coloring benchmarks, use the scripts provided in the GitHub repository:
+
+```bash
+cd datasets
+python gen_graphs_coloring.py
+```
+
+## Citation
+
+If you use this benchmark in your research, please cite:
+
+```bibtex
+@article{lucibello2026benchmarking,
+  title={Benchmarking Graph Neural Networks in Solving Hard Constraint Satisfaction Problems},
+  author={Lucibello, Carlo and others},
+  journal={arXiv preprint arXiv:2602.18419},
+  year={2026}
+}
+```